Probabilistic Graphical Models for Computer Vision  

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Probabilistic Graphical Models for Computer Vision
( 2016-01-12 )


Speaker: Qiang Ji
Time: 2015-08-17 9:00
Place: Classroom 3A101, No.3 Teach Building, West Campus

Probabilistic graphical models (PGMs) have been increasingly applied to solving many computer vision problems, thanks mainly to their powerful capability in representing various types of visual knowledge as well as to the availability of principled statistical theories and algorithms for inference and learning with PGMs. Under probabilistic models, data are modeled as a collection of random variables with a particular pattern of possible dependencies among them. Using the model, we can then discover knowledge, predict future events, and infer hidden causes. In this lecture, I will first introduce different types of PGMs including directed PGMs such as Bayesian Networks, Hidden Markov Models, and Dynamic Bayesian Networks as well as undirected PGMs such as Markov Networks, Conditional Random Fields, and Restricted Boltzmann Machine. I will then discuss various PGM learning and inference methods, including both parameter and structure learning as well as both exact and approximate inference methods. The second part of this lecture will cover the applications of PGMs to computer vision. These applications include image segmentation, facial expression recognition, object tracking, and human action/activity recognition. This lecture will conclude with a discussion of remaining issues and future directions in PGM research and in its application in computer vision.

Short Bio:
Qiang Ji received his Ph.D degree in Electrical Engineering from the University of Washington. He is currently a Professor with the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI). From 2009 to 2010, he served as a program director at the National Science Foundation (NSF), where he managed NSF’s computer vision and machine learning programs. He also held teaching and research positions with the Beckman Institute at University of Illinois at Urbana-Champaign, the Robotics Institute at Carnegie Mellon University, the Dept. of Computer Science at University of Nevada, and the US Air Force Research Laboratory. Prof. Ji currently serves as the director of the Intelligent Systems Laboratory (ISL) at RPI.
Prof. Ji's research interests are in computer vision, probabilistic graphical models, machine learning, and their applications in various fields. He has published over 200 papers in peer-reviewed journals and conferences, and has received multiple awards for his work. Prof.Ji is an editor on several related IEEE and international journals and he has served as a general chair, program chair, technical area chair, and program committee member for numerous international conferences/workshops. Prof. Ji is a fellow of the IEEE and the IAPR.